Facial recognition allows computing systems to detect and/or identify faces in images. By implementing facial recognition, services can be personalized to an identified user. However, facial recognition techniques may often consider sensitive attributes, or features, such as gender, age, and/or race. For example, conventional deep learning models used for facial recognition may encode these sensitive attributes in a mathematical space, which may affect an output of the model, and in some use cases may therefore affect the personalization of services or treatment of individuals. It is thus desirable to remove opportunities for any biases attributed to sensitive attributes from affecting the output of a model. Therefore, there is a need for facial recognition techniques that do not consider sensitive attributes.